# ruff: noqa: E501
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import json
from dataclasses import fields
from enum import Enum
from typing import TYPE_CHECKING, Any

import jsonschema
import pytest
import regex as re
import torch
from pydantic import BaseModel

from tests.reasoning.utils import run_reasoning_extraction
from vllm.config import StructuredOutputsConfig
from vllm.distributed import cleanup_dist_env_and_memory
from vllm.entrypoints.llm import LLM
from vllm.outputs import RequestOutput
from vllm.platforms import current_platform
from vllm.reasoning.abs_reasoning_parsers import ReasoningParserManager
from vllm.sampling_params import (
    GuidedDecodingParams,
    SamplingParams,
    StructuredOutputsParams,
)

if TYPE_CHECKING:
    from vllm.config.model import TokenizerMode
else:
    TokenizerMode = str

NGRAM_SPEC_CONFIG = {
    "model": "[ngram]",
    "num_speculative_tokens": 5,
    "prompt_lookup_max": 5,
    "prompt_lookup_min": 1,
}

EAGLE_SPEC_CONFIG = {
    "method": "eagle",
    "model": "yuhuili/EAGLE-LLaMA3.1-Instruct-8B",
    "num_speculative_tokens": 5,
}

PARAMS_MODELS_BACKENDS_TOKENIZER_MODE = [
    ("mistralai/Ministral-8B-Instruct-2410", "xgrammar", "auto", None),
    # FIXME: Since "auto" will use Mistral tokenizer and these backends do not support
    # it, we skip these tests for now.
    # ("mistralai/Ministral-8B-Instruct-2410", "guidance", "auto", None),
    # ("mistralai/Ministral-8B-Instruct-2410", "lm-format-enforcer", "auto", None),
    ("mistralai/Ministral-8B-Instruct-2410", "guidance", "hf", None),
    pytest.param(
        "mistralai/Ministral-8B-Instruct-2410",
        "lm-format-enforcer",
        "hf",
        None,
        marks=pytest.mark.skip(
            reason=(
                "Flaky: lm-format-enforcer intermittently returns"
                "incomplete JSON."
                "See https://github.com/noamgat/lm-format-enforcer/issues/169"
            )
        ),
    ),
    ("mistralai/Ministral-8B-Instruct-2410", "xgrammar", "mistral", None),
    ("Qwen/Qwen2.5-1.5B-Instruct", "xgrammar", "auto", None),
    pytest.param(
        "Qwen/Qwen2.5-1.5B-Instruct",
        "lm-format-enforcer",
        "auto",
        None,
        marks=pytest.mark.skip(
            reason=(
                "Flaky: lm-format-enforcer intermittently returns"
                "incomplete JSON."
                "See https://github.com/noamgat/lm-format-enforcer/issues/169"
            )
        ),
    ),
    # FIXME: This tests are flaky on CI thus disabled. Tracking in Issue #24402
    # ("mistralai/Ministral-8B-Instruct-2410", "outlines", "auto", None),
    # ("mistralai/Ministral-8B-Instruct-2410", "outlines", "mistral", None),
    # ("Qwen/Qwen2.5-1.5B-Instruct", "guidance", "auto"),
    ("mistralai/Ministral-8B-Instruct-2410", "outlines", "auto", NGRAM_SPEC_CONFIG),
    ("mistralai/Ministral-8B-Instruct-2410", "guidance", "hf", NGRAM_SPEC_CONFIG),
    ("Qwen/Qwen2.5-1.5B-Instruct", "xgrammar", "auto", NGRAM_SPEC_CONFIG),
    ("meta-llama/Meta-Llama-3.1-8B-Instruct", "xgrammar", "auto", EAGLE_SPEC_CONFIG),
]

PARAMS_MODELS_TOKENIZER_MODE = [
    ("mistralai/Ministral-8B-Instruct-2410", "auto"),
    ("Qwen/Qwen2.5-1.5B-Instruct", "auto"),
]


class CarType(str, Enum):
    sedan = "sedan"
    suv = "SUV"
    truck = "Truck"
    coupe = "Coupe"


class CarDescription(BaseModel):
    brand: str
    model: str
    car_type: CarType


def test_guided_decoding_deprecated():
    with pytest.warns(DeprecationWarning, match="GuidedDecodingParams is deprecated.*"):
        guided_decoding = GuidedDecodingParams(json_object=True)

    structured_outputs = StructuredOutputsParams(json_object=True)
    assert fields(guided_decoding) == fields(structured_outputs)

    with pytest.warns(DeprecationWarning, match="guided_decoding is deprecated.*"):
        sp1 = SamplingParams(guided_decoding=guided_decoding)

    with pytest.warns(DeprecationWarning, match="guided_decoding is deprecated.*"):
        sp2 = SamplingParams.from_optional(guided_decoding=guided_decoding)

    assert sp1 == sp2
    assert sp1.structured_outputs == guided_decoding


@pytest.mark.parametrize(
    "model_name, backend, tokenizer_mode, speculative_config",
    PARAMS_MODELS_BACKENDS_TOKENIZER_MODE,
)
def test_structured_output(
    sample_json_schema: dict[str, Any],
    unsupported_json_schema: dict[str, Any],
    sample_sql_ebnf: str,
    sample_sql_lark: str,
    sample_regex: str,
    sample_structured_outputs_choices: str,
    backend: str,
    tokenizer_mode: str,
    model_name: str,
    speculative_config: dict[str, Any],
):
    if current_platform.is_tpu() and speculative_config:
        pytest.skip("TPU does not support speculative decoding")

    # Use a single LLM instance for several scenarios to
    # speed up the test suite.
    llm = LLM(
        model=model_name,
        enforce_eager=True,
        max_model_len=1024,
        structured_outputs_config=dict(
            backend=backend, disable_any_whitespace=backend in {"xgrammar", "guidance"}
        ),
        seed=120,
        tokenizer_mode=tokenizer_mode,
        load_format="auto" if not model_name.startswith("mistralai/") else "hf",
        config_format="auto" if not model_name.startswith("mistralai/") else "hf",
        speculative_config=speculative_config,
    )

    #
    # Test 1: Generate JSON output based on a provided schema
    #
    sampling_params = SamplingParams(
        temperature=1.0,
        max_tokens=4096,
        structured_outputs=StructuredOutputsParams(json=sample_json_schema),
    )

    prompt = (
        "Give an example JSON for an employee profile that fits this "
        "schema. Make the response as short as possible. Schema: "
        f"{sample_json_schema}"
    )
    outputs = llm.generate(
        [prompt] * 2,
        sampling_params=sampling_params,
        use_tqdm=True,
    )

    assert outputs is not None

    for output in outputs:
        assert output is not None
        assert isinstance(output, RequestOutput)
        prompt = output.prompt

        generated_text = output.outputs[0].text
        assert generated_text is not None
        if backend != "lm-format-enforcer":
            assert "\n" not in generated_text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
        try:
            output_json = json.loads(generated_text)
        except json.JSONDecodeError as e:
            pytest.fail(
                f"Invalid JSON from backend={backend}: {generated_text!r}\n"
                f"Schema: {sample_json_schema}\nError: {e}"
            )
        jsonschema.validate(instance=output_json, schema=sample_json_schema)

    #
    # Test 2: Generate JSON object without a schema
    #
    if backend != "outlines":
        sampling_params = SamplingParams(
            temperature=1.0,
            max_tokens=4096,
            n=2,
            structured_outputs=StructuredOutputsParams(json_object=True),
        )

        outputs = llm.generate(
            prompts=(
                "Generate a JSON object with curly braces for a person with "
                "name and age fields for John Smith who is 31 years old. "
                "Make the response as short as possible."
            ),
            sampling_params=sampling_params,
            use_tqdm=True,
        )

        assert outputs is not None
        for output in outputs:
            assert output is not None
            assert isinstance(output, RequestOutput)

            for i in range(2):
                generated_text = output.outputs[i].text
                print(generated_text)
                assert generated_text is not None

                # Parse to verify it is a valid JSON object
                parsed_json = json.loads(generated_text)
                assert isinstance(parsed_json, dict)

    #
    # Test 3: test a jsonschema incompatible with xgrammar
    #
    sampling_params = SamplingParams(
        temperature=1.0,
        max_tokens=4096,
        structured_outputs=StructuredOutputsParams(json=unsupported_json_schema),
    )
    if backend.startswith("xgrammar"):
        with pytest.raises(
            ValueError,
            match="The provided JSON schema contains features "
            "not supported by xgrammar.",
        ):
            prompt = (
                f"Give an example JSON for an employee profile that "
                f"fits this schema: {unsupported_json_schema}. "
                f"Make the response as short as possible."
            )
            llm.generate(
                [prompt] * 2,
                sampling_params=sampling_params,
                use_tqdm=True,
            )
    else:
        prompt = (
            f"Give an example JSON object for a grade that "
            f"fits this schema: {unsupported_json_schema}. "
            f"Make the response as short as possible."
        )
        outputs = llm.generate(
            prompt,
            sampling_params=sampling_params,
            use_tqdm=True,
        )
        assert outputs is not None
        for output in outputs:
            assert output is not None
            assert isinstance(output, RequestOutput)
            generated_text = output.outputs[0].text
            assert generated_text is not None
            print(generated_text)

            # Parse to verify it is valid JSON
            parsed_json = json.loads(generated_text)
            assert isinstance(parsed_json, dict)

    if backend not in ["outlines", "lm-format-enforcer"]:
        #
        # Test 4: Generate SQL statement using EBNF grammar
        #
        sampling_params = SamplingParams(
            temperature=0.8,
            top_p=0.95,
            max_tokens=1000,
            structured_outputs=StructuredOutputsParams(grammar=sample_sql_ebnf),
        )
        outputs = llm.generate(
            (
                "Generate a sql statement that selects col_1 from "
                "table_1 where it is equal to 1. Make the response as short as "
                "possible."
            ),
            sampling_params=sampling_params,
            use_tqdm=True,
        )

        assert outputs is not None
        for output in outputs:
            assert output is not None
            assert isinstance(output, RequestOutput)
            prompt = output.prompt

            generated_text = output.outputs[0].text
            assert generated_text is not None

            # remove spaces for comparison b/c we removed them in the grammar
            ground_truth = "SELECT col_1 from table_1 where col_1 = 1".replace(" ", "")

            assert generated_text.strip() == ground_truth

            print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

        #
        # Test 5: Generate SQL statement using Lark grammar
        #
        sampling_params = SamplingParams(
            temperature=0.8,
            top_p=0.95,
            max_tokens=1000,
            structured_outputs=StructuredOutputsParams(grammar=sample_sql_lark),
        )
        outputs = llm.generate(
            (
                "Generate a sql statement that selects col_1 from "
                "table_1 where it is equal to 1. Make the response as short as "
                "possible."
            ),
            sampling_params=sampling_params,
            use_tqdm=True,
        )

        assert outputs is not None
        for output in outputs:
            assert output is not None
            assert isinstance(output, RequestOutput)
            prompt = output.prompt

            generated_text = output.outputs[0].text
            assert generated_text is not None

            # use Lark to parse the output, and make sure it's a valid parse tree
            from lark import Lark

            parser = Lark(sample_sql_lark)
            parser.parse(generated_text)

            # remove spaces for comparison b/c we removed them in the grammar
            ground_truth = "SELECT col_1 from table_1 where col_1 = 1".replace(" ", "")

            assert generated_text.strip() == ground_truth

            print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

        #
        # Test 6: Test invalid grammar input
        #
        sampling_params = SamplingParams(
            temperature=0.8,
            top_p=0.95,
            max_tokens=1000,
            structured_outputs=StructuredOutputsParams(grammar="not a grammar"),
        )
        with pytest.raises(ValueError, match="Failed to convert the grammar "):
            llm.generate(
                (
                    "Generate a sql statement that selects col_1 from "
                    "table_1 where it is equal to 1. Make the response as short "
                    "as possible."
                ),
                sampling_params=sampling_params,
                use_tqdm=True,
            )

    #
    # Test 7: Generate text based on a regex pattern
    #
    sampling_params = SamplingParams(
        temperature=0.8,
        top_p=0.95,
        structured_outputs=StructuredOutputsParams(regex=sample_regex),
    )

    prompt = (
        f"Give an example IPv4 address with this regex: {sample_regex}. "
        f"Make the response as short as possible."
    )
    outputs = llm.generate(
        [prompt] * 2,
        sampling_params=sampling_params,
        use_tqdm=True,
    )

    assert outputs is not None
    for output in outputs:
        assert output is not None
        assert isinstance(output, RequestOutput)
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(generated_text)
        assert generated_text is not None
        assert re.fullmatch(sample_regex, generated_text) is not None
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

    #
    # Test 8: Generate text based on a choices
    #
    sampling_params = SamplingParams(
        temperature=0.8,
        top_p=0.95,
        structured_outputs=StructuredOutputsParams(
            choice=sample_structured_outputs_choices
        ),
    )

    outputs = llm.generate(
        (
            "The best language for type-safe systems programming is "
            "(Make the response as short as possible.) "
        ),
        sampling_params=sampling_params,
        use_tqdm=True,
    )
    assert outputs is not None
    for output in outputs:
        assert output is not None
        assert isinstance(output, RequestOutput)
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(generated_text)
        assert generated_text is not None
        assert generated_text in sample_structured_outputs_choices
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

    #
    # Test 9: Generate structured output using a Pydantic model with an enum
    #
    json_schema = CarDescription.model_json_schema()
    sampling_params = SamplingParams(
        temperature=1.0,
        max_tokens=1000,
        structured_outputs=StructuredOutputsParams(json=json_schema),
    )

    outputs = llm.generate(
        (
            "Generate a JSON with the brand, model and car_type of the most "
            "iconic car from the 90's. Make the response as short as "
            "possible."
        ),
        sampling_params=sampling_params,
        use_tqdm=True,
    )

    assert outputs is not None

    for output in outputs:
        assert output is not None
        assert isinstance(output, RequestOutput)
        prompt = output.prompt

        generated_text = output.outputs[0].text
        assert generated_text is not None
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
        try:
            output_json = json.loads(generated_text)
        except json.JSONDecodeError as e:
            pytest.fail(
                f"Invalid JSON from backend={backend}: {generated_text!r}\n"
                f"Schema: {json_schema}\nError: {e}"
            )
        jsonschema.validate(instance=output_json, schema=json_schema)

    #
    # Test 10: Generate structured with minLength and maxLength
    #
    min_length = 50
    max_length = 50
    json_schema = {
        "type": "object",
        "properties": {
            "description": {
                "type": "string",
                "maxLength": max_length,
                "minLength": min_length,
            }
        },
        "required": ["description"],
        "additionalProperties": False,
    }

    sampling_params = SamplingParams(
        temperature=1.0,
        max_tokens=4096,
        structured_outputs=StructuredOutputsParams(json=json_schema),
    )

    outputs = llm.generate(
        (
            "Generate a description of a frog using 50 characters. "
            "Make the response as short as possible."
        ),
        sampling_params=sampling_params,
        use_tqdm=True,
    )

    assert outputs is not None

    for output in outputs:
        assert output is not None
        assert isinstance(output, RequestOutput)
        prompt = output.prompt

        generated_text = output.outputs[0].text
        assert generated_text is not None
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
        try:
            output_json = json.loads(generated_text)
        except json.JSONDecodeError as e:
            pytest.fail(
                f"Invalid JSON from backend={backend}: {generated_text!r}\n"
                f"Schema: {json_schema}\nError: {e}"
            )
        jsonschema.validate(instance=output_json, schema=json_schema)

    if backend not in ["outlines", "lm-format-enforcer"]:
        #
        # Test 11: Generate structured output using structural_tag format
        #
        structural_tag_config = {
            "type": "structural_tag",
            "structures": [
                {
                    "begin": "<function=get_weather>",
                    "schema": {
                        "type": "object",
                        "properties": {"city": {"type": "string"}},
                        "additionalProperties": False,
                    },
                    "end": "</function>",
                }
            ],
            "triggers": ["<function="],
        }

        sampling_params = SamplingParams(
            temperature=0.0,
            max_tokens=4096,
            structured_outputs=StructuredOutputsParams(
                structural_tag=json.dumps(structural_tag_config)
            ),
        )

        prompt = """
You have access to the following function to retrieve the weather in a city:

    {
        "name": "get_weather",
        "parameters": {
            "city": {
                "param_type": "string",
                "description": "The city to get the weather for",
                "required": True
            }
        }
    }

If a you choose to call a function ONLY reply in the following format:
<{start_tag}={function_name}>{parameters}{end_tag}
where

start_tag => `<function`
parameters => a JSON dict with the function argument name
            as key and function argument value as value.
end_tag => `</function>`

Here is an example,
<function=example_function_name>{"example_name": "example_value"}</function>

Reminder:
- Function calls MUST follow the specified format
- Required parameters MUST be specified
- Only call one function at a time
- Put the entire function call reply on one line
- Always add your sources when using search results to answer the user query

You are a helpful assistant.

Given the previous instructions, what is the weather in New York City? \
Make the response as short as possible.
"""

        # Change this once other backends support structural_tag
        outputs = llm.generate(prompt, sampling_params=sampling_params, use_tqdm=True)
        assert outputs is not None

        for output in outputs:
            assert output is not None
            assert isinstance(output, RequestOutput)
            generated_text = output.outputs[0].text
            assert generated_text is not None

            # Search for function call pattern in the response
            function_call_pattern = r"<function=get_weather>(.*?)</function>"
            matches = re.findall(function_call_pattern, generated_text)

            if not matches:
                print(
                    f"Warning: No function calls found in response: {generated_text!r}"
                )
                continue

            # Take the first function call if multiple are found
            json_str = matches[0]
            try:
                json_content = json.loads(json_str)
                assert "city" in json_content
                assert isinstance(json_content["city"], str)
                print(f"Found valid function call: {generated_text!r}")
            except (json.JSONDecodeError, AssertionError) as e:
                pytest.fail(
                    f"Invalid function call format: {generated_text!r}\nError: {str(e)}"
                )


@pytest.mark.parametrize(
    "model_name, backend, tokenizer_mode, reasoning_parser, speculative_config",  # noqa: E501
    [
        (
            "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
            "xgrammar",
            "auto",
            "deepseek_r1",
            NGRAM_SPEC_CONFIG,
        ),
        ("Qwen/Qwen3-1.7B", "xgrammar", "auto", "deepseek_r1", None),
    ],
)
def test_structured_output_with_reasoning_matrices(
    backend: str,
    tokenizer_mode: TokenizerMode,
    reasoning_parser: str,
    model_name: str,
    speculative_config: dict[str, Any] | None,
):
    if current_platform.is_tpu() and speculative_config:
        pytest.skip("TPU does not support speculative decoding")

    # Use a single LLM instance for several scenarios to
    # speed up the test suite.
    llm = LLM(
        model=model_name,
        # Don't use eager execution on TPUs because we want to test for no
        # recompilation at runtime
        enforce_eager=bool(not current_platform.is_tpu()),
        max_model_len=1024,
        max_num_seqs=16,
        structured_outputs_config=dict(
            backend=backend,
            disable_any_whitespace=backend in {"xgrammar", "guidance"},
            reasoning_parser=reasoning_parser,
        ),
        tokenizer_mode=tokenizer_mode,
        speculative_config=speculative_config,
    )
    tokenizer = llm.get_tokenizer()
    reasoner = ReasoningParserManager.get_reasoning_parser(reasoning_parser)(
        tokenizer=tokenizer
    )

    reasoning_prompt = "Solve the following math problem step-by-step, then provide the final answer as JSON object with a single key 'result'. Make sure to correct your reasoning if there are any issue should it arise.\nProblem: What is 5 * 8 + 2?"  # noqa: E501
    reasoning_schema = {
        "type": "object",
        "properties": {"result": {"type": "integer"}},
        "required": ["result"],
        "additionalProperties": False,
    }
    if "Qwen3" in model_name:
        reasoning_prompt += "<think>\n"

    sampling_params = SamplingParams(
        temperature=0.1,
        max_tokens=8192,
        structured_outputs=StructuredOutputsParams(json=reasoning_schema),
    )
    outputs = llm.generate(
        [reasoning_prompt],
        sampling_params=sampling_params,
        use_tqdm=True,
    )

    assert outputs is not None
    output = outputs[0]
    assert output is not None and isinstance(output, RequestOutput)
    prompt = output.prompt
    generated_text = output.outputs[0].text
    reasoning, content = run_reasoning_extraction(reasoner, [generated_text])
    print(f"Prompt: {prompt!r}\nReasoning: {reasoning!r}\nContent: {content!r}")

    if "Qwen3" in model_name:
        assert content is not None

    assert reasoning is not None

    if content is not None:
        output_json = json.loads(content)
        jsonschema.validate(instance=output_json, schema=reasoning_schema)


@pytest.mark.parametrize("model_name, tokenizer_mode", PARAMS_MODELS_TOKENIZER_MODE)
def test_structured_output_auto_mode(
    unsupported_json_schema: dict[str, Any],
    model_name: str,
    tokenizer_mode: str,
):
    llm = LLM(
        model=model_name,
        max_model_len=1024,
        structured_outputs_config=dict(backend="auto"),
        tokenizer_mode=tokenizer_mode,
        load_format="auto",
        config_format="auto",
    )

    sampling_params = SamplingParams(
        temperature=1.0,
        max_tokens=1000,
        structured_outputs=StructuredOutputsParams(json=unsupported_json_schema),
    )

    prompts = (
        "Give an example JSON object for a grade "
        "that fits this schema: "
        f"{unsupported_json_schema}. Make the response as short as possible."
    )
    # This would fail with the default of "xgrammar", but in "auto"
    # we will handle fallback automatically.
    outputs = llm.generate(prompts, sampling_params=sampling_params, use_tqdm=True)
    # Make sure `auto` backend handling doesn't mess up sampling_params
    # and that we can reuse it without error.
    outputs.extend(
        llm.generate(prompts, sampling_params=sampling_params, use_tqdm=True)
    )

    assert outputs is not None
    for output in outputs:
        assert output is not None
        assert isinstance(output, RequestOutput)
        generated_text = output.outputs[0].text
        assert generated_text is not None
        print(generated_text)

        # Parse to verify it is valid JSON
        parsed_json = json.loads(generated_text)
        assert isinstance(parsed_json, dict)


def test_guidance_no_additional_properties():
    llm = LLM(
        model="Qwen/Qwen2.5-1.5B-Instruct",
        max_model_len=1024,
        structured_outputs_config=dict(
            backend="guidance",
            disable_any_whitespace=True,
            disable_additional_properties=True,
        ),
    )

    schema = {
        "type": "object",
        "properties": {
            "a1": {"type": "string"},
            "a2": {"type": "string"},
            "a3": {"type": "string"},
        },
        "required": ["a1", "a2", "a3"],
    }

    prompt = (
        "<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a "
        "helpful assistant.<|im_end|>\n<|im_start|>user\nPlease generate a "
        "large JSON object with key-value pairs a1=b1, a2=b2, ..., a20=b20. "
        "Make the response as short as possible."
        "<|im_end|>\n<|im_start|>assistant\n"
    )

    def generate_with_backend(backend):
        structured_outputs_params = StructuredOutputsParams(
            json=schema,
            backend=backend,
            disable_any_whitespace=True,
            disable_additional_properties=True,
        )
        sampling_params = SamplingParams(
            temperature=0, max_tokens=256, structured_outputs=structured_outputs_params
        )

        outputs = llm.generate(prompt, sampling_params=sampling_params)
        assert outputs is not None
        generated_text = outputs[0].outputs[0].text
        assert generated_text is not None
        parsed_json = json.loads(generated_text)
        assert isinstance(parsed_json, dict)
        jsonschema.validate(instance=parsed_json, schema=schema)
        return parsed_json

    generated = generate_with_backend("guidance")
    assert "a1" in generated
    assert "a2" in generated
    assert "a3" in generated
    assert "a4" not in generated
    assert "a5" not in generated
    assert "a6" not in generated


@pytest.mark.parametrize("backend", ["guidance", "xgrammar", "outlines"])
def test_structured_output_batched_with_non_structured_outputs_requests(
    sample_json_schema: dict[str, Any],
    backend: str,
):
    # Don't use eager execution on TPUs because we want to test for no
    # recompilation at runtime
    enforce_eager = bool(not current_platform.is_tpu())

    llm = LLM(
        model="meta-llama/Meta-Llama-3.1-8B-Instruct",
        enforce_eager=enforce_eager,
        max_model_len=1024,
        structured_outputs_config=StructuredOutputsConfig(
            backend=backend,
            disable_any_whitespace=backend in {"xgrammar", "guidance"},
        ),
    )

    structured_outputs_prompt = (
        "Give an example JSON for an employee profile that fits this "
        "schema. Make the response as short as possible. Schema: "
        f"{sample_json_schema}"
    )

    non_structured_outputs_prompt = "The diameter of the Earth in kilometers is "

    prompts = [structured_outputs_prompt, non_structured_outputs_prompt]
    sampling_params = [
        SamplingParams(
            temperature=1.0,
            max_tokens=400,
            structured_outputs=StructuredOutputsParams(json=sample_json_schema),
        ),
        # No max tokens, temp=0 to assert on contents
        SamplingParams(
            seed=42,
            temperature=0,
            top_p=1.0,
        ),
    ]

    outputs = llm.generate(
        prompts=prompts, sampling_params=sampling_params, use_tqdm=True
    )

    assert outputs is not None

    # Free memory as soon as possible as failed assertions
    # will short circuit and not free up memory
    del llm
    torch.cuda.empty_cache()
    cleanup_dist_env_and_memory()

    for index, output in enumerate(outputs):
        assert output is not None
        assert isinstance(output, RequestOutput)
        prompt = output.prompt

        generated_text = output.outputs[0].text
        assert generated_text is not None
        print(f"Prompt:\n{prompt!r}\nGenerated text:\n{generated_text!r}")

        if index == 0:
            # First prompt is structured outputs, expect valid JSON
            assert "\n" not in generated_text
            output_json = json.loads(generated_text)
            jsonschema.validate(instance=output_json, schema=sample_json_schema)
        else:
            # Second prompt is not structured outputs, expect valid output
            # Cannot assert on exact output, but we can expect it to be factual
            assert "12,742" in generated_text

            # non-structured outputs requests should not return a valid JSON here
            with pytest.raises(ValueError):
                output_json = json.loads(generated_text)


@pytest.mark.parametrize("guided_decoding_backend", ["xgrammar"])
def test_structured_output_with_structural_tag(
    guided_decoding_backend: str,
):
    llm = LLM(
        model="Qwen/Qwen2.5-1.5B-Instruct",
        guided_decoding_backend=guided_decoding_backend,
    )

    structural_tag_config = {
        "type": "structural_tag",
        "format": {
            "type": "triggered_tags",
            "tags": [
                {"begin": "hello_flag", "content": {"type": "any_text"}, "end": "hello"}
            ],
            "triggers": ["hello"],
            "stop_after_first": False,
        },
    }

    sampling_params = SamplingParams(
        temperature=0.0,
        max_tokens=500,
        guided_decoding=StructuredOutputsParams(
            structural_tag=json.dumps(structural_tag_config)
        ),
    )

    prompt = "Hello and repete hello 10 times, do not say anything else. Only say hello hello hello, now start"
    outputs = llm.generate(prompt, sampling_params=sampling_params, use_tqdm=True)
    assert outputs is not None
    for output in outputs:
        assert output is not None
        assert isinstance(output, RequestOutput)
        prompt = output.prompt
        generated_text = output.outputs[0].text
        assert generated_text is not None
        assert "hello_flag" in generated_text, (
            f"Expected 'hello_flag' to be in generated text, but got: {generated_text}"
        )
